Cristiana Bassani

Aerosol optical retrieval and surface reflectance from airborne remote sensing data over land.

Quantitative analysis of atmospheric optical properties and surface reflectance can be performed by applying radiative transfer theory in the Atmosphere-Earth coupled system, for the atmospheric correction of hyperspectral remote sensing data. This paper describes a new physically-based algorithm to retrieve the aerosol optical thickness at 550nm (τ550) and the surface reflectance (ρ) from airborne acquired data in the atmospheric window of the Visible and Near-Infrared (VNIR) range. The algorithm is realized in two modules. Module A retrieves τ550 with a minimization algorithm, then Module B retrieves the surface reflectance ρ for each pixel of the image. The method was tested on five remote sensing images acquired by an airborne sensor under different geometric conditions to evaluate the reliability of the method. The results, τ550 and ρ, retrieved from each image were validated with field data contemporaneously acquired by a sun-sky radiometer and a spectroradiometer, respectively. Good correlation index, r, and low root mean square deviations, RMSD, were obtained for the τ550 retrieved by Module A (r2 = 0.75, RMSD = 0.08) and the ρ retrieved by Module B (r2 ≤ 0.9, RMSD ≤ 0.003). Overall, the results are encouraging, indicating that the method is reliable for optical atmospheric studies and the atmospheric correction of airborne hyperspectral images. The method does not require additional at-ground measurements about at-ground reflectance of the reference pixel and aerosol optical thickness.

Sensitivity analysis of a bio-optical model for Italian lakes focused on Landsat-8, Sentinel-2 and Sentinel-3

Abstract We analysed the sensitivity of a Case-2 bio-optical model where the water reflectance is computed as a function of concentrations of three optical water quality parameters (WQPs) of three Italian lakes (Garda, Mantua and Trasimeno) and their specific absorption and backscattering coefficients. The modelled reflectance is computed based on the spectral characteristics of three optical sensors, on-board Landsat-8, Sentinel-2 and Sentinel-3. The variance-based analysis was able to quantify the lake-dependence for all (50,000 runs) the simulated reflectance. The results confirmed that Sentinel-3 water reflectance is sensitive to WQPs in all the trophic conditions investigated.

Anthropogenic activities monitoring by a multi-sensor approach: a case study of Rossano (CS) landfill

In this paper the landfill located to Rossano (CS) is analyzed through an integrated approach by the use of different remote sensed data from 1994 to 2010. In particular, the Multispectral Visible and Infrared Imaging Spectrometer (MIVIS) airborne sensor, orthophotos and Google Earthtm satellite image are used. The integrated multi-temporal data and downscaling analysis have identified several evidences such as the increase of land consumption. The Normalized Difference Vegetation Index (NDVI) of MIVIS image allows to observe the distribution of exposed soil and vegetated areas. At the same time, MIVIS thermal data shows some superficial thermal anomalies which highlighted these changes. Finally, this study shows how this approach can be useful to support both monitoring studies of land consumption as well as planning environmental campaigns in such areas.

Evaluation of adjacency effect for MIVIS airborne images

This work is aimed to atmospherically correct remote sensing data in the solar spectral domain (Visible and Near Infrared) allowing the better assessment of the surface spectral material characteristics. This was obtained by the inversion of the radiative transfer equation for at-sensor signal. In order to detect targets with peculiar spectral characteristics, the atmospheric correction has to take into account the diffuse radiation that constitutes a significant component to the at sensor radiance. The effect of this component (namely adjacency effect), which tends to mask the pixel seen by the sensor, derives principally from the atmospheric scattering due to the aerosol loading in the scene. At this purpose an algorithm based on 6S calculation was defined to derive the direct and diffuse component of the radiation required to determine the contribution to the pixel reflectance related to the surrounding pixels. The developed algorithm allowed the assessment of this environmental contribution besides the pixel reflectance. Such application, on airborne hyperspectral sensor MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) scenes, leads to obtain accurate pixel reflectance if compared with ground measurements acquired within testing areas. This work shows how adjacency effect has a significant role in the correction of remote sensing data, especially if acquired by an airborne hyperspectral sensor. The preliminary analysis of the results have highlighted that the adjacency effect is not negligible, mainly when pixels in the scene are spectrally heterogeneous.

Biomass evaluation by the use of Landsat satellite imagery and forestry data

Satellite imagery allows to estimate vegetation parameters related to large areas and to evaluate biogeochemical cycles and radiative energy transfer processes between soil/vegetation and atmosphere.Moreover, the spectral indices derived from remote sensed data can be used for biomass estimation.This paper focuses on the evaluation of above-ground biomass in the Leonessa Municipality, Latium Region (Italy) by the use of Landsat 7 ETM+ (2001) and Landsat 8-OLI (2015) data. To achieve this goal, Rural Development Programs (PSR) and Forest Management Plans(FMP) (2001-2010) have been analyzed to retrieve the main information related to the different types of wood resources. In particular, dendrometry and prospects of different cultivation classes provide the main data such as the extension (ha), the biomass production (m3/ha), the number of plants, the cuts plan of each Forest Management Unit (FMU). This dataset was organized within a Geographical Information System (GIS) as well as Landsat images.Landsat 7 imagery was classified with two spectral indices, Normalized Difference Vegetation Index (NDVI) and Tasseled Cup, in order to find a correlation between remote sensed data and biomass production in m3/ha. Once obtained the spectral model, the analysis was extended to Landsat 8 and the 2015 biomass map was produced and exported on the web. The results, obtained by the exclusively analysis of open source optical remote sensing data, demonstrate their suitability to update FMPs with lower cost if compared to canonical field methods. Additionally, the analysis allows to extend the investigation to un-analyzed areas by forestry studies, too.

Analysis of anthropic activities by optical remote sensing data at different spatial, spectral and temporal resolutions

This paper describes a remote sensing based downscaling approach for the analysis of the area affected by anthropic activities as quarrying and landfill. We selected the South-East flank of Mt. Vesuvius National Park as study area because in the last decades there have been a strong anthropic pressure with mining and municipal solid waste dumping as the main activities. The changes occurred were analysed by optical remote sensing at different spatial and spectral resolution. These activities had an environmental impact that is highlighted by integration of multi-source data.The multi- and hyper-spectral remote sensing data were adopted to study spectral indices and spatial patterns. The spectral response of targets supported the interpretation of stress conditions and other environmental anomalies in specific zones. Landsat, MIVIS, aerial photos and thematic maps were fused in a GIS for environmental analysis providing some Warning Zones defined in core and neighbouring of the anthropic area.

Exploitation of hyperspectral data for infrastructures status assessment: Preliminary results of the istimes test bed

The paper presents the preliminary results of the experimental campaign conducted within the FP7 ISTIMES project. In particular the present paper deals with the exploitation of the ground based hyperspectral potentialities for detecting the conservation status of infrastructures, such as reinforced cement structures, roads, buildings, and architectural heritage. To exploit the effectiveness of hyperspectral technology, in situ measurements were carried out at the Montagnole (French Alpes) test site using the HySpex hyperspectral scanner (VNIR-SWIR) on a reinforced concrete beam solicited with different loading factors just to force the formation of a clear cracking structure within the concrete beam. The preliminary results obtained by the application of standard processing techniques, such as RX, MNF and change detection methods showed that suitable information are obtainable by processing hyperspectral data.

Evaluation of a hyperspectral scanner allowing for deterioration status assessment of asbestos-cement roofing sheets

Aim of this study is the identification of the hyperspectral scanner operational characteristics allowing for asbestos cement (AC) roofing sheets deterioration status assessment that is related to the asbestos fibers abundance. At this purpose we made laboratory measurements on AC samples with different deterioration status collected in two industrial areas in Italy. The asbestos occurrence in the AC samples was recognized using XRD and FTIR instruments and the abundance of surfacing asbestos fibers was performed by using a high resolution scanner (SEM). The samples optical characteristics and the directional effects that can affect the AC samples were analyzed using a portable field spectrometer (ASD). The results of the ASD measurements (i.e. band-depth ratio of the continuum removed calculated for the asbestos diagnostic band at 2.32μm) were related to the relative percentage of surfacing asbestos fibers (i.e. the AC deterioration status). Since laboratory measurements confirmed that optical measurements are sensitive to variations in asbestos fiber abundance, detection limit analysis was used for defining the requirements (signal-to-noise ratio, band FWHM, and sampling range) of an optimal hyperspectral sensor most suitable for detecting the diagnostic asbestos absorption features.